Scene matching refers to the process of matching a region in one image with the corresponding region in another image, where both image regions are part of the same scene. Scene matching plays an important role in determining the location of a digital image, since most digital images lack detailed location information. Even though some recent cameras have the ability to determine location using global positioning system (GPS) technology, only a small fraction of the digital images being captured today include location information. In the absence of this location information, the location at which a digital image was captured can often be determined by identifying unique objects in the stationary background that can be matched to images having a known location using a scene matching algorithm.
Earlier work on scene matching involved computing correlation between images. However, in addition to being very computationally intensive, these methods cannot handle the large variations in scale, lighting, and pose encountered in consumer images. There has been recent work on matching feature-rich complex scenes using scale-invariant features (SIFT) as described by Lowe in the article “Distinctive image features from scale invariant keypoints” (Intl. Journal of Computer Vision, Vol. 60, pp. 91-110, 2004). Other similar approaches include the SURF algorithm disclosed by Bay et al. in the article “SURF: Speeded up robust features” (Computer Vision and Image Understanding, Vol. 110, pp. 346-359, 2008) and the PCA-SIFT algorithm disclose by Ke et al. in the article “PCA-SIFT: A more distinctive representation for local image descriptors” (Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2004). However, using these techniques to match and register every image in an image database is a time-consuming process when a large image database is involved.
There remains a need for an efficient scene matching algorithm that can be used to identify digital images having matching backgrounds from a collection of digital images.